data augmentation does for generalization: artificially expands training set

How can you teach a computer to see better?

Imagine you're teaching your friend to recognize cats. You show them pictures of cats with different backgrounds, lighting, and angles. But after a while, they start to forget the cats' unique features because everything looks too similar.

To prevent your friend from forgetting, you could show them more varied pictures of cats. This way, they learn to spot cats even if they're in different situations. This is what data augmentation does for machine learning models.

Example

You show your friend 10 pictures of cats, then 10 more with slight changes like a different background or angle. After seeing 20 pictures, they can spot cats in new pictures better.

Remember this

Data augmentation helps machine learning models generalize better by exposing them to more varied examples.

Text adapted from Wikipedia, licensed under CC BY-SA 4.0.

Related concepts

batch size affects generalization: larger batches find sharper minima

Larger batch sizes lead to sharper minima, enhancing generalization by providing more accurate gradient estimates

Autoencoder

Can you imagine creating new photos that look like old ones?

Deep learning

Why do smart phones sometimes struggle to recognize new faces?

Overfitting

Why does fitting too much detail to data ruin predictions?

Federated learning

Ever wonder how to train a smart device without sharing your personal data?

Unsupervised learning

How can we find hidden patterns in massive, unlabeled data?

Swipe through more Machine Learning concepts

Open Pocket Polymath